Autors: Tsakoumis, A. C., Vladov, S. S., Mladenov, V. M.
Title: Electric load forecasting with multilayer perceptron and Elman neural network
Keywords: Load forecasting , Multilayer perceptrons , Neural networks

References

    Issue

    6th Seminar on Neural Network Applications in Electrical Engineering, NEUREL 2002 - Proceedings, pp. 87-90, 2002, Serbia, IEEE, DOI 10.1109/NEUREL.2002.1057974

    Цитирания (Citation/s):
    1. Almalaq, A., Zhang, J.J. Deep learning application: Load forecasting in big data of smart grids., Studies in Computational Intelligence, Vol. 865, DOI: 10.1007/978-3-030-31760-7_4, pp. 103-128 - 2020 - в издания, индексирани в Scopus или Web of Science
    2. Sharifzadeh, Mahdi, Alexandra Sikinioti-Lock, and Nilay Shah. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression." Renewable and Sustainable Energy Reviews 108 (2019): 513-538. - 2019 - в издания, индексирани в Scopus или Web of Science
    3. Pombo, José Álvaro Nunes. "Modelos optimizados para sistemas de miniprodução híbridos instalados em edifícios e áreas envolventes." (2018). - 2018 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    4. Singh, Navneet Kumar, Asheesh Kumar Singh, and Manoj Tripathy. "A comparative study of BPNN, RBFNN and ELMAN neural network for short-term electric load forecasting: A case study of Delhi region." In 2014 9th international conference on industrial and information systems (ICIIS), pp. 1-6. IEEE, 2014. - 2014 - в издания, индексирани в Scopus или Web of Science
    5. Yang, Jin-Fang, Yong-Jie Zhai, Da-Ping Xu, and Pu Han. "SMO algorithm applied in time series model building and forecast." In 2007 International Conference on Machine Learning and Cybernetics, vol. 4, pp. 2395-2400. IEEE, 2007. - 2007 - в издания, индексирани в Scopus или Web of Science
    6. Xia, Changhao, Bangjun Lei, Hongping Wang, and Jiangnan Li. "GRNN short-term load forecasting model and virtual instrument design." Energy Procedia 13 (2011): 9150-9158. - 2011 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    7. Singh, Navneet Kumar, Asheesh Kumar Singh, and Manoj Tripathy. "Short-term load/price forecasting in deregulated electric environment using ELMAN neural network." In 2015 International Conference on Energy Economics and Environment (ICEEE), pp. 1-6. IEEE, 2015. - 2015 - в издания, индексирани в Scopus или Web of Science
    8. Zhou, Hongli, Ge Guo, and Manqiang Liu. "GA-aided Elman neural network controller for behavior-based robot." In 2006 6th World Congress on Intelligent Control and Automation, vol. 2, pp. 9068-9072. IEEE, 2006. - 2006 - в издания, индексирани в Scopus или Web of Science
    9. Galarniotis, A. I., A. C. Tsakoumis, P. Fessas, S. S. Vladov, and V. M. Mladenov. "Using elman and fir neural networks for short term electric load forecasting." In Signals, Circuits and Systems, 2003. SCS 2003. International Symposium on, vol. 2, pp. 433-436. IEEE, 2003. - 2003 - в издания, индексирани в Scopus или Web of Science
    10. Guirelli, Cleber Roberto. "Previsão da carga de curto prazo de áreas elétricas através de técnicas de inteligência artificial." PhD diss., Universidade de São Paulo, 2006. - 2006 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    11. Hong, Chan-Young, Jung-Hoon Park, Tae-Sung Yoon, and Jin-Bae Park. "A study on the bayesian recurrent neural network for time series prediction." Journal of Institute of Control, Robotics and Systems 10, no. 12 (2004): 1295-1304. - 2004 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    12. Singh, Navneet Kumar, Asheesh Kumar Singh, and Pradeep Kumar. "PSO optimized radial basis function neural network based electric load forecasting model." In 2014 Australasian Universities Power Engineering Conference (AUPEC), pp. 1-6. IEEE, 2014. - 2014 - в издания, индексирани в Scopus или Web of Science
    13. Debusschere, Vincent, and Seddik Bacha. "Five forecasting algorithms for energy consumption in Vietnam." In 2013 IEEE Grenoble Conference, pp. 1-8. IEEE, 2013. - 2013 - в издания, индексирани в Scopus или Web of Science
    14. Chang, Hui-Kuo, Hsing-Chia Kuo, and Yen-Zen Wang. "Novel grey model for diesel engine oil monitoring." Journal of ship research 50, no. 01 (2006): 31-37. - 2006 - в издания, индексирани в Scopus или Web of Science
    15. Rashid, Tarik A. "A Novel Recurrent Neural Network Model: A Case Study in Energy Load Forecasting." PhD diss., University College Dublin, 2006. - 2006 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    16. Kirilov, S. and Zaykov, I., A Neural Network with HfO2 Memristors. - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    17. 761) Hou, H., Liu, C., Wang, Q., Wu, X., Tang, J., Shi, Y. and Xie, C., 2022. Review of load forecasting based on artificial intelligence methodologies, models, and challenges. Electric Power Systems Research, 210, p.108067. (Google Scholar, Scopus) - 2022 - в издания, индексирани в Scopus или Web of Science
    18. Pratapa Raju, M. and Jaya Laxmi, A., 2022. Implementation of Load Demand Prediction Model for a Domestic Load Center Using Different Machine Learning Algorithms—A Comparison. In Pervasive Computing and Social Networking (pp. 445-467). Springer, Singapore. doi.org/10.1007/978-981-16-5640-8_35, ISBN 978-981-16-5640-8, (Scopus, Web of Science) - 2022 - в издания, индексирани в Scopus или Web of Science
    19. Taha, A., Barakat, B., Taha, M.M., Shawky, M.A., Lai, C.S., Hussain, S., Abideen, M.Z. and Abbasi, Q.H., 2023. A Comparative Study of Single and Multi-Stage Forecasting Algorithms for the Prediction of Electricity Consumption Using a UK-National Health Service (NHS) Hospital Dataset. Future Internet, vol. 15, issue (4), pp. 1 – 17, https://doi.org/10.3390/fi15040134 (Scopus, Google Scholar) - 2023 - в издания, индексирани в Scopus или Web of Science
    20. Khodayar, M. and Regan, J., 2023. Deep Neural Networks in Power Systems: A Review. Energies, vol. 16, issue (12), No. 4773, pp. 1-38, https://doi.org/10.3390/en16124773 (Web of Science, Scopus, Google Scholar) IF 3.2. - 2023 - в издания, индексирани в Scopus или Web of Science
    21. 罗敏, 杨劲锋, 俞蕙, 赖雨辰, 郭杨运, 周尚礼, 向睿, 童星 and 陈潇, 基于 TPE 优化集成学习的短期负荷预测方法 (网络首发). 上海交通大学学报, p.0. 2023, (Google Scholar) - 2023 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    22. Nagvanshi, S.S., Kaur, I. (2023). Forecasting of COVID-19 Cases in India Using Machine Learning: A Critical Analysis. In: Khanna, A., Gupta, D., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Third Doctoral Symposium on Computational Intelligence. Lecture Notes in Networks and Systems, vol 479. Springer, Singapore, pp. 593 - 601. https://doi.org/10.1007/978-981-19-3148-2_51 (Web of Science, Google Scholar) - 2023 - в издания, индексирани в Scopus или Web of Science
    23. Ling, W., Sun, Y., Li, Q., Lin, J., Hu, J., Liang, Z. and Xiong, L., 2023, September. A deep learning short-term load forecasting method for extreme scenarios. In Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023) ISSN 0277786X, ISBN 978-151066833-1, DOI 10.1117/12.2689861 (Vol. 12790, pp. 335-341). SPIE. (Scopus, Google Scholar) - 2023 - в издания, индексирани в Scopus или Web of Science
    24. 罗敏, 杨劲锋, 俞蕙, 赖雨辰, 郭杨运, 周尚礼, ... & 陈潇. (2024). 基于树结构 Parzen 估计器优化集成学习的短期负荷预测方法. 上海交通大学学报, vol. 58, issue (6), 819. (Google Scholar) - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science

    Вид: постер/презентация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science